{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "de7dbe44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/Users/khador/Documents', '/usr/local/Cellar/python@3.9/3.9.0_2/Frameworks/Python.framework/Versions/3.9/lib/python39.zip', '/usr/local/Cellar/python@3.9/3.9.0_2/Frameworks/Python.framework/Versions/3.9/lib/python3.9', '/usr/local/Cellar/python@3.9/3.9.0_2/Frameworks/Python.framework/Versions/3.9/lib/python3.9/lib-dynload', '', '/usr/local/lib/python3.9/site-packages', '/usr/local/Cellar/protobuf/3.14.0/libexec/lib/python3.9/site-packages', '/usr/local/lib/python3.9/site-packages/IPython/extensions', '/Users/khador/.ipython']\n"
     ]
    },
    {
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   ],
   "source": [
    "%matplotlib inline\n",
    "import torch\n",
    "import torchvision\n",
    "from torch import nn\n",
    "from d2l import torch as d2l\n",
    "import sys \n",
    "\n",
    "print(sys.path)\n",
    "\n",
    "d2l.set_figsize()\n",
    "img = d2l.Image.open('/Users/khador/Documents/img/testphoto.jpg')#输入自己的文件路径\n",
    "d2l.plt.imshow(img);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a806246b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply(img, aug, num_rows=2, num_cols=4, scale=1.5):\n",
    "    Y = [aug(img) for _ in range(num_rows * num_cols)]\n",
    "    d2l.show_images(Y, num_rows, num_cols, scale=scale)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "e606b61c",
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   "outputs": [
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      "text/plain": [
       "<Figure size 432x216 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "apply(img, torchvision.transforms.RandomHorizontalFlip())#水平方向随机反转"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e6b43845",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "<Figure size 432x216 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "apply(img, torchvision.transforms.RandomVerticalFlip())#垂直翻转"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aeeba831",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 432x216 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shape_aug = torchvision.transforms.RandomResizedCrop(\n",
    "    (200, 200), scale=(0.1, 1), ratio=(0.5, 2))#随机取部分并调整至规定大小\n",
    "apply(img, shape_aug)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "906b4c1e",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "<Figure size 432x216 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "apply(img, torchvision.transforms.ColorJitter(\n",
    "    brightness=0.5, contrast=0, saturation=0, hue=0))#亮度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4085ef25",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 432x216 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "apply(img, torchvision.transforms.ColorJitter(\n",
    "    brightness=0, contrast=0, saturation=0, hue=0.5))#色相"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "156df2ee",
   "metadata": {},
   "outputs": [
    {
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       "   <rect height=\"74.127273\" width=\"55.595455\" x=\"185.378261\" y=\"96.152727\"/>\n",
       "  </clipPath>\n",
       "  <clipPath id=\"p612afbcd2c\">\n",
       "   <rect height=\"74.127273\" width=\"55.595455\" x=\"272.717391\" y=\"96.152727\"/>\n",
       "  </clipPath>\n",
       " </defs>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 432x216 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_aug = torchvision.transforms.ColorJitter(\n",
    "    brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)#多个参数一起调\n",
    "apply(img, color_aug)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c382f668",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <dc:date>2022-01-26T15:49:53.409244</dc:date>\n",
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   "source": [
    "augs = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.RandomVerticalFlip(),\n",
    "    color_aug, shape_aug])#所有策略一起使用\n",
    "apply(img, augs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "06efdc2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /Users/khador/Documents/data/cifar-10-python.tar.gz\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "100.0%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting /Users/khador/Documents/data/cifar-10-python.tar.gz to /Users/khador/Documents/data\n"
     ]
    },
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   "source": [
    "all_images = torchvision.datasets.CIFAR10(\n",
    "    train=True, root=\"/Users/khador/Documents/data\", download=True)\n",
    "d2l.show_images([\n",
    "    all_images[i][0] for i in range(32)], 4, 8, scale=0.8);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "14a819bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#训练集做数据增强，测试集不用（但命名仍带‘augs'注意区分）\n",
    "train_augs = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.RandomHorizontalFlip(),#这里只做水平翻转\n",
    "    torchvision.transforms.ToTensor()])\n",
    "\n",
    "test_augs = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.ToTensor()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e68c53d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_cifar10(is_train, augs, batch_size):\n",
    "    dataset = torchvision.datasets.CIFAR10(\n",
    "        root=\"/Users/khador/Documents/data\", train=is_train,\n",
    "        transform=augs, download=True)#augs是之前定义的数据处理方法\n",
    "    dataloader = torch.utils.data.DataLoader(\n",
    "        dataset, batch_size=batch_size, shuffle=is_train,\n",
    "        num_workers=4)#num_workers可以稍大些，数据增广计算量可能很大\n",
    "    return dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3f786de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#训练函数\n",
    "def train_batch_ch13(net, X, y, loss, trainer, devices):\n",
    "    \"\"\"用多GPU进行小批量训练\"\"\"\n",
    "    if isinstance(X, list):\n",
    "        # 微调BERT中所需（稍后讨论）\n",
    "        X = [x.to(devices[0]) for x in X]\n",
    "    else:\n",
    "        X = X.to(devices[0])\n",
    "    y = y.to(devices[0])\n",
    "    net.train()\n",
    "    trainer.zero_grad()\n",
    "    pred = net(X)\n",
    "    l = loss(pred, y)\n",
    "    l.sum().backward()\n",
    "    trainer.step()\n",
    "    train_loss_sum = l.sum()\n",
    "    train_acc_sum = d2l.accuracy(pred, y)\n",
    "    return train_loss_sum, train_acc_sum\n",
    "\n",
    "#@save\n",
    "def train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs,\n",
    "               devices=d2l.try_all_gpus()):\n",
    "    \"\"\"用多GPU进行模型训练\"\"\"\n",
    "    timer, num_batches = d2l.Timer(), len(train_iter)\n",
    "    animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1],\n",
    "                            legend=['train loss', 'train acc', 'test acc'])\n",
    "    net = nn.DataParallel(net, device_ids=devices).to(devices[0])\n",
    "    for epoch in range(num_epochs):\n",
    "        # 4个维度：储存训练损失，训练准确度，实例数，特点数\n",
    "        metric = d2l.Accumulator(4)\n",
    "        for i, (features, labels) in enumerate(train_iter):\n",
    "            timer.start()\n",
    "            l, acc = train_batch_ch13(\n",
    "                net, features, labels, loss, trainer, devices)\n",
    "            metric.add(l, acc, labels.shape[0], labels.numel())\n",
    "            timer.stop()\n",
    "            if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:\n",
    "                animator.add(epoch + (i + 1) / num_batches,\n",
    "                             (metric[0] / metric[2], metric[1] / metric[3],\n",
    "                              None))\n",
    "        test_acc = d2l.evaluate_accuracy_gpu(net, test_iter)\n",
    "        animator.add(epoch + 1, (None, None, test_acc))\n",
    "    print(f'loss {metric[0] / metric[2]:.3f}, train acc '\n",
    "          f'{metric[1] / metric[3]:.3f}, test acc {test_acc:.3f}')\n",
    "    print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec on '\n",
    "          f'{str(devices)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c1332aca",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size, devices, net = 256, d2l.try_all_gpus(), d2l.resnet18(10, 3)\n",
    "\n",
    "def init_weights(m):\n",
    "    if type(m) in [nn.Linear, nn.Conv2d]:\n",
    "        nn.init.xavier_uniform_(m.weight)#xavier初始化\n",
    "\n",
    "net.apply(init_weights)\n",
    "\n",
    "def train_with_data_aug(train_augs, test_augs, net, lr=0.001):\n",
    "    train_iter = load_cifar10(True, train_augs, batch_size)\n",
    "    test_iter = load_cifar10(False, test_augs, batch_size)\n",
    "    loss = nn.CrossEntropyLoss(reduction=\"none\")\n",
    "    trainer = torch.optim.Adam(net.parameters(), lr=lr)\n",
    "    train_ch13(net, train_iter, test_iter, loss, trainer, 10, devices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d1d2b90",
   "metadata": {},
   "outputs": [
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